Research and Implementation of Short Video Product Recommendation Algorithm Based on Multimodal Features

Abstract

With the rapid development of the Internet and the rapid improvement of smartphone technology, a large number of short videos are shared through social platforms every minute, so video content analysis is currently a very important and popular job in machine learning and artificial intelligence. In recent years, in the field of domestic Internet content, platforms such as Toutiao, Netease, Yidian Information, and Baijiahao have sprung up. These content distribution platforms utilize multi-channel content sources, and the amount of content has exploded, of which short video content accounts for a large proportion. The rich short video content has changed the behavior of users and provided users with a more convenient way to socialize. Short video has a higher dimension of information presentation, and it is easier for people to accept and spread. At present, a large amount of short video information has appeared on the Internet. These short video information have brought serious information overload problems to users, and also brought huge challenges to short video operators and video editors. This paper adopts the method of multimodal fusion, which has high accuracy in identifying emotional states such as happiness, sadness, anger, and disgust in the sample.